Linear Discriminant Analysis and Its Application in Plant Classification

Du, Minggang; Wang, Xianfeng

doi:10.1109/icic.2011.147

Cited by 4 publications

(1 citation statement)

References 24 publications

(27 reference statements)

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“…This The recognition of plants is very important to study the genetic diversity of plant (Singh et al, 2011;Lu et al, 2012), ecological sensitivity, environmental durability, the maintenance of atmospheric composition, nutrient cycle and other ecosystem processes (Du and Wang, 2011). Some researchers developed the recognition of plant to manage food production (Wu et al, 2009), to recognize medicinal plants (Ershad, 2011;Pornpanomchai et al, 2011a), to manage and control the growth of plants (Chuanyu et al, 2011) and to meet the objectives of research in the field of botany.…”

Section: Introductionmentioning

confidence: 99%

Plant Recognition Using Stereo Leaf Image Using Gray-Level Co-Occurrence Matrix

Syahputra

Harjoko

Wardoyo

et al. 2014

Journal of Computer Science

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Adequate knowledge, such as information about the unique characteristics of each plant, is necessary to identify plant. Researchers have made plant recognition based on leaf characteristics. The leaf image-based plant recognition in view of different angles is a new challenge. In this study, the research on the plant recognition was conducted based on leaf images resulted from 3D stereo camera. The 3D images are very influential in the development of computer vision theory, which can provide more detailed information of an object. One of the information that can be obtained is about the position of the object in its image with the background as well as of the camera. One of the ways used to obtain such information is to calculate the disparity. However, this method will only tell the position of the object compared to other objects without that of range. Sum Absolute Different (SAD) is a method that can be used to find the disparity value. The SAD method does not require heavy computations and long process. Before calculating the disparity, all the images should be previously segmented. The objective of this segmentation is to separate all the objects from the background. Furthermore, filtering and polynomial transformation at the results of disparity is necessary to improve the quality of resultant images. Furthermore, 22 features were extracted using GLCM features (second order statistics) of images resulted from disparity improvement. The highest accuracy of match in the recognition of plant varieties was obtained at 50 cm distance and in the recognition of three plant varieties was 83.3%.

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Section: Introductionmentioning

confidence: 99%

Plant Recognition Using Stereo Leaf Image Using Gray-Level Co-Occurrence Matrix

Syahputra

Harjoko

Wardoyo

et al. 2014

Journal of Computer Science

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Enhanced HOG-LBP Feature Vector Based on Leaf Image for Plant Species Identification

Bhalchandra

Behare

2021

Communications in Computer and Information Science

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Plant Species Identification Using Computer Vision Techniques: A Systematic Literature Review

Wäldchen

Mäder

2017

Arch Computat Methods Eng

317

193

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Species knowledge is essential for protecting biodiversity. The identification of plants by conventional keys is complex, time consuming, and due to the use of specific botanical terms frustrating for non-experts. This creates a hard to overcome hurdle for novices interested in acquiring species knowledge. Today, there is an increasing interest in automating the process of species identification. The availability and ubiquity of relevant technologies, such as, digital cameras and mobile devices, the remote access to databases, new techniques in image processing and pattern recognition let the idea of automated species identification become reality. This paper is the first systematic literature review with the aim of a thorough analysis and comparison of primary studies on computer vision approaches for plant species identification. We identified 120 peer-reviewed studies, selected through a multi-stage process, published in the last 10 years (2005–2015). After a careful analysis of these studies, we describe the applied methods categorized according to the studied plant organ, and the studied features, i.e., shape, texture, color, margin, and vein structure. Furthermore, we compare methods based on classification accuracy achieved on publicly available datasets. Our results are relevant to researches in ecology as well as computer vision for their ongoing research. The systematic and concise overview will also be helpful for beginners in those research fields, as they can use the comparable analyses of applied methods as a guide in this complex activity.

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Linear Discriminant Analysis and Its Application in Plant Classification

Cited by 4 publications

References 24 publications

Plant Recognition Using Stereo Leaf Image Using Gray-Level Co-Occurrence Matrix

Plant Recognition Using Stereo Leaf Image Using Gray-Level Co-Occurrence Matrix

Enhanced HOG-LBP Feature Vector Based on Leaf Image for Plant Species Identification

Plant Species Identification Using Computer Vision Techniques: A Systematic Literature Review

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